Add cirq.StabilizerSampler #3355
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Strilanc
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Sep 23, 2020
Strilanc
commented
- Minimal interface
- Supports some random operations (depolarize, probabilistic gates)
- This is a potential replacement for cirq.CliffordSimulator (which is in a bad state right now, tracking two redundant state objects in a fashion observable to callers)
- Minimal interface - Supports some random operations (depolarize, probabilistic gates) - This is a potential replacement for cirq.CliffordSimulator (which is in a bad state right now, tracking two redundant state objects in a fashion observable to callers)
cirq/ops/common_channels.py
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| @@ -183,6 +183,17 @@ def __repr__(self) -> str: | |||
| def __str__(self) -> str: | |||
| return 'depolarize(p={!r})'.format(self._p) | |||
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| def _act_on_(self, args): | |||
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nit: missing type annotations
| } | ||
| axes_map = {q: i for i, q in enumerate(circuit.all_qubits())} | ||
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| for _ in range(repetitions): |
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Question: Why not propagate the stabilizer once and sample from the resulting stabilizer state at the end? I suppose it's simply deferred to a follow-up?
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Measurements can return random results which play out in different ways. In principle we could check if all measurements are terminal, or translate the circuit into a graph dataflow form that was faster to sampler from (assuming all classically controlled operations were Paulis). I wasn't planning to do these optimizations.
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The optimization would mostly target circuits with terminal measurements. We could also support measurements commuting with stabilizer, but that may be excessive and not useful. Finally, in some applications you want probability of a prescribed (Pauli) measurement result and in this case there is a unique update to the stabilizer generators consistent with the result.
…c_cliffords # Conflicts: # cirq/ops/common_channels_test.py
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